Ebay, Incorporated

Yuyangzi Fu

Jingying Wang

Xiaoyuan Wu

Gurudatta Horantur Shivaswamy

Yi Yu

Zihao Jiang

Kaikai Tong

Maoquan Wang

Xueyuan Jiang

Linyin Wu

Manish Harshad Shah

Atcharavan Vajarapong

Shuya Li

A first variant of a listing and a second variant of the listing are received. The listing describes an item for sale in an electronic marketplace. The first variant describes a different iteration of the item relative to the second variant. First user activity data of the first variant is generated and second user activity data of the second variant is generated. The first user activity data corresponds to user input metrics associated with the first variant. The second user activity data corresponds to user input metrics associated with the second variant. A search engine receives a first query. Based at least in part on the first user activity data relative to the second user activity data, a first search result associated with the first variant is ranked higher than a second search result associated with the second variant.

System, method, and computer storage media for employing user activity data of variants for improved search

Computer information retrieval by automatically marking at least a first item listing, of a first set of item listings, as a candidate for removal as a search result for a query. Such automatic marking occurs in response to receiving an indication that a selection has been made at a computing device, where the selection is at least partially indicative of the user requesting removal, from a set of search results, of a first item listing based on a particular attribute value associated with the first item listing.

Intelligent computer search engine removal of search results

A recommendation system leverages multi-target search to provide item listing recommendations and/or query suggestions. For a given input image with multiple objects, multi-target search uses object detection to detect each object, and stores complementary object data associating each object from the image. Additionally, a search of an item listing datastore is performed using each object from the image as a search query. Based on item listings returned as search results, complementary item listings data associating item listings is stored. In some configurations, the complementary item listings data is also used to train a machine learning model to predict complementary item listings for a given item listing. When an input item listing is received, item listing recommendations and/or query suggestions are determined for the input item listing using the complementary object data, the complementary item listing data, and/or the machine learning model.

Complementary item recommendation system

Virtual environment arrangement and configuration techniques are described. In one example, virtualization techniques are employed to generate a virtual environment as implemented via a virtual reality platform by one or more computing devices. The virtual environment includes virtual stores arranged along virtual streets. Virtual digital content is included in the stores, such as to initiate conversion of a good or service represented by the content. Configuration of the virtual environment is based on user, provider, and/or machine learning inputs.

Virtual environment arrangement and configuration

Growing community of inventors

Nantong, China

Yuyangzi Fu